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http://dx.doi.org/10.6109/jicce.2013.11.1.030

Real-Time Stochastic Optimum Control of Traffic Signals  

Lee, Hee-Hyol (Graduate School of Information, Production, and Systems, Waseda University)
Publication Information
Journal of information and communication convergence engineering / v.11, no.1, 2013 , pp. 30-44 More about this Journal
Abstract
Traffic congestion has become a serious problem with the recent exponential increase in the number of vehicles. In urban areas, almost all traffic congestion occurs at intersections. One of the ways to solve this problem is road expansion, but it is difficult to realize in urban areas because of the high cost and long construction period. In such cases, traffic signal control is a reasonable method for reducing traffic jams. In an actual situation, the traffic flow changes randomly and its randomness makes the control of traffic signals difficult. A prediction of traffic jams is, therefore, necessary and effective for reducing traffic jams. In addition, an autonomous distributed (stand-alone) point control of each traffic light individually is better than the wide and/or line control of traffic lights from the perspective of real-time control. This paper describes a stochastic optimum control of crossroads and multi-way traffic signals. First, a stochastic model of traffic flows and traffic jams is constructed by using a Bayesian network. Secondly, the probabilistic distributions of the traffic flows are estimated by using a cellular automaton, and then the probabilistic distributions of traffic jams are predicted. Thirdly, optimum traffic signals of crossroads and multi-way intersection are searched by using a modified particle swarm optimization algorithm to realize real-time traffic control. Finally, simulations are carried out to confirm the effectiveness of the real-time stochastic optimum control of traffic signals.
Keywords
Traffic signal; Traffic jam; Stochastic Optimum Control; Bayesian Network; Cellular Automaton; Particle Swarm Optimization; Genetic Algorithm;
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